Overview

Dataset statistics

Number of variables18
Number of observations7799
Missing cells36113
Missing cells (%)25.7%
Duplicate rows204
Duplicate rows (%)2.6%
Total size in memory5.1 MiB
Average record size in memory684.4 B

Variable types

Categorical6
Numeric8
Text4

Alerts

Dataset has 204 (2.6%) duplicate rowsDuplicates
Baths is highly overall correlated with Bed Rooms and 2 other fieldsHigh correlation
Bed Rooms is highly overall correlated with Baths and 2 other fieldsHigh correlation
Building Type is highly overall correlated with GarageHigh correlation
Exposure is highly overall correlated with Selling StatusHigh correlation
Floor # is highly overall correlated with Selling StatusHigh correlation
Garage is highly overall correlated with Building Type and 3 other fieldsHigh correlation
Interior Size SqFt is highly overall correlated with Baths and 3 other fieldsHigh correlation
Lot Size Ft is highly overall correlated with GarageHigh correlation
Price $ is highly overall correlated with Baths and 2 other fieldsHigh correlation
Selling Status is highly overall correlated with Exposure and 1 other fieldsHigh correlation
Type is highly overall correlated with GarageHigh correlation
Selling Status is highly imbalanced (72.3%)Imbalance
Lot Size Ft has 4043 (51.8%) missing valuesMissing
Balcony Size SqFt has 6648 (85.2%) missing valuesMissing
Garage has 7687 (98.6%) missing valuesMissing
Floor # has 7551 (96.8%) missing valuesMissing
Exposure has 7612 (97.6%) missing valuesMissing
Community Name has 367 (4.7%) missing valuesMissing
Ceiling avg has 2187 (28.0%) missing valuesMissing

Reproduction

Analysis started2024-04-23 11:03:15.523948
Analysis finished2024-04-23 11:03:36.894247
Duration21.37 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Type
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size539.1 KiB
Detached Two Story
3750 
Suite
1655 
Detached Bungalow
996 
Two Story
992 
Semi-Detached Two Story
 
144
Other values (4)
 
262

Length

Max length23
Median length22
Mean length13.766124
Min length4

Characters and Unicode

Total characters107362
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTwo Story
2nd rowTwo Story
3rd rowTwo Story
4th rowTwo Story
5th rowTwo Story

Common Values

ValueCountFrequency (%)
Detached Two Story 3750
48.1%
Suite 1655
21.2%
Detached Bungalow 996
 
12.8%
Two Story 992
 
12.7%
Semi-Detached Two Story 144
 
1.8%
Bungalow 120
 
1.5%
Penthouse 109
 
1.4%
Semi-Detached Bungalow 19
 
0.2%
Loft 14
 
0.2%

Length

2024-04-23T11:03:37.068386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T11:03:37.390661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
two 4886
27.8%
story 4886
27.8%
detached 4746
27.0%
suite 1655
 
9.4%
bungalow 1135
 
6.5%
semi-detached 163
 
0.9%
penthouse 109
 
0.6%
loft 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 11854
11.0%
t 11573
 
10.8%
o 11030
 
10.3%
9795
 
9.1%
S 6704
 
6.2%
a 6044
 
5.6%
w 6021
 
5.6%
h 5018
 
4.7%
D 4909
 
4.6%
c 4909
 
4.6%
Other values (16) 29505
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11854
11.0%
t 11573
 
10.8%
o 11030
 
10.3%
9795
 
9.1%
S 6704
 
6.2%
a 6044
 
5.6%
w 6021
 
5.6%
h 5018
 
4.7%
D 4909
 
4.6%
c 4909
 
4.6%
Other values (16) 29505
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11854
11.0%
t 11573
 
10.8%
o 11030
 
10.3%
9795
 
9.1%
S 6704
 
6.2%
a 6044
 
5.6%
w 6021
 
5.6%
h 5018
 
4.7%
D 4909
 
4.6%
c 4909
 
4.6%
Other values (16) 29505
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11854
11.0%
t 11573
 
10.8%
o 11030
 
10.3%
9795
 
9.1%
S 6704
 
6.2%
a 6044
 
5.6%
w 6021
 
5.6%
h 5018
 
4.7%
D 4909
 
4.6%
c 4909
 
4.6%
Other values (16) 29505
27.5%

Bed Rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1040518
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:37.671496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0143755
Coefficient of variation (CV)0.32679078
Kurtosis-0.45203595
Mean3.1040518
Median Absolute Deviation (MAD)1
Skewness-0.24340296
Sum24208.5
Variance1.0289577
MonotonicityNot monotonic
2024-04-23T11:03:37.874350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 2800
35.9%
3 2231
28.6%
2 1300
16.7%
1 415
 
5.3%
1.5 317
 
4.1%
5 315
 
4.0%
2.5 297
 
3.8%
3.5 93
 
1.2%
6 22
 
0.3%
4.5 6
 
0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
1 415
 
5.3%
1.5 317
 
4.1%
2 1300
16.7%
2.5 297
 
3.8%
3 2231
28.6%
3.5 93
 
1.2%
4 2800
35.9%
4.5 6
 
0.1%
5 315
 
4.0%
6 22
 
0.3%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 1
 
< 0.1%
6 22
 
0.3%
5 315
 
4.0%
4.5 6
 
0.1%
4 2800
35.9%
3.5 93
 
1.2%
3 2231
28.6%
2.5 297
 
3.8%
2 1300
16.7%

Baths
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2915758
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:38.085679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81494729
Coefficient of variation (CV)0.35562746
Kurtosis1.169167
Mean2.2915758
Median Absolute Deviation (MAD)0
Skewness0.90117357
Sum17872
Variance0.66413908
MonotonicityNot monotonic
2024-04-23T11:03:38.296861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 4593
58.9%
3 1652
 
21.2%
1 866
 
11.1%
4 582
 
7.5%
5 100
 
1.3%
6 6
 
0.1%
ValueCountFrequency (%)
1 866
 
11.1%
2 4593
58.9%
3 1652
 
21.2%
4 582
 
7.5%
5 100
 
1.3%
6 6
 
0.1%
ValueCountFrequency (%)
6 6
 
0.1%
5 100
 
1.3%
4 582
 
7.5%
3 1652
 
21.2%
2 4593
58.9%
1 866
 
11.1%

Interior Size SqFt
Real number (ℝ)

HIGH CORRELATION 

Distinct2846
Distinct (%)36.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2039.9879
Minimum263
Maximum6973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:38.773787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum263
5-th percentile578
Q11340
median1987.5
Q32673.25
95-th percentile3698.15
Maximum6973
Range6710
Interquartile range (IQR)1333.25

Descriptive statistics

Standard deviation976.48833
Coefficient of variation (CV)0.47867358
Kurtosis0.43625043
Mean2039.9879
Median Absolute Deviation (MAD)668.5
Skewness0.53792211
Sum15907826
Variance953529.46
MonotonicityNot monotonic
2024-04-23T11:03:39.056588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 24
 
0.3%
2000 24
 
0.3%
1740 22
 
0.3%
1800 20
 
0.3%
2250 16
 
0.2%
2788 16
 
0.2%
1600 16
 
0.2%
2240 16
 
0.2%
1285 16
 
0.2%
1700 15
 
0.2%
Other values (2836) 7613
97.6%
ValueCountFrequency (%)
263 1
< 0.1%
275 2
< 0.1%
300 1
< 0.1%
306 2
< 0.1%
307 1
< 0.1%
314 1
< 0.1%
321 1
< 0.1%
325 1
< 0.1%
329 1
< 0.1%
332 1
< 0.1%
ValueCountFrequency (%)
6973 1
< 0.1%
6217 1
< 0.1%
6160 1
< 0.1%
6135 1
< 0.1%
6059 1
< 0.1%
6055 1
< 0.1%
6033 1
< 0.1%
6032 1
< 0.1%
6010 1
< 0.1%
6005 2
< 0.1%

Lot Size Ft
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)1.3%
Missing4043
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean41.818211
Minimum14
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:39.341847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile26
Q136
median40
Q345
95-th percentile60
Maximum150
Range136
Interquartile range (IQR)9

Descriptive statistics

Standard deviation12.104507
Coefficient of variation (CV)0.28945539
Kurtosis26.397268
Mean41.818211
Median Absolute Deviation (MAD)5
Skewness3.3322001
Sum157069.2
Variance146.51908
MonotonicityNot monotonic
2024-04-23T11:03:39.642120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 540
 
6.9%
45 431
 
5.5%
36 396
 
5.1%
40 392
 
5.0%
38 284
 
3.6%
30 169
 
2.2%
35 154
 
2.0%
42 140
 
1.8%
44 116
 
1.5%
32 106
 
1.4%
Other values (40) 1028
 
13.2%
(Missing) 4043
51.8%
ValueCountFrequency (%)
14 3
 
< 0.1%
18 12
 
0.2%
19 3
 
< 0.1%
20 71
0.9%
21 14
 
0.2%
22 20
 
0.3%
23 8
 
0.1%
24 2
 
< 0.1%
24.6 7
 
0.1%
25 43
0.6%
ValueCountFrequency (%)
150 16
 
0.2%
100 4
 
0.1%
80 2
 
< 0.1%
70 87
1.1%
66 1
 
< 0.1%
65 22
 
0.3%
64 5
 
0.1%
62 21
 
0.3%
61 4
 
0.1%
60 74
0.9%

Price $
Real number (ℝ)

HIGH CORRELATION 

Distinct3298
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1234431.5
Minimum319900
Maximum9819990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:39.927441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum319900
5-th percentile547810
Q1794900
median1044900
Q31454945
95-th percentile2480481
Maximum9819990
Range9500090
Interquartile range (IQR)660045

Descriptive statistics

Standard deviation685658.47
Coefficient of variation (CV)0.55544474
Kurtosis11.892586
Mean1234431.5
Median Absolute Deviation (MAD)300090
Skewness2.4923694
Sum9.6273312 × 109
Variance4.7012754 × 1011
MonotonicityNot monotonic
2024-04-23T11:03:40.223427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799900 43
 
0.6%
899900 37
 
0.5%
849900 28
 
0.4%
769900 27
 
0.3%
729900 27
 
0.3%
699900 26
 
0.3%
739900 24
 
0.3%
599900 24
 
0.3%
749900 22
 
0.3%
719900 22
 
0.3%
Other values (3288) 7519
96.4%
ValueCountFrequency (%)
319900 1
< 0.1%
325500 1
< 0.1%
328900 1
< 0.1%
339000 2
< 0.1%
349000 2
< 0.1%
358794 1
< 0.1%
359900 1
< 0.1%
364900 1
< 0.1%
369000 1
< 0.1%
369900 2
< 0.1%
ValueCountFrequency (%)
9819990 1
< 0.1%
8900000 1
< 0.1%
6509800 1
< 0.1%
6449900 1
< 0.1%
6399900 1
< 0.1%
6349900 1
< 0.1%
6249900 1
< 0.1%
6140000 1
< 0.1%
5880000 1
< 0.1%
5760000 1
< 0.1%

Balcony Size SqFt
Real number (ℝ)

MISSING 

Distinct326
Distinct (%)28.3%
Missing6648
Missing (%)85.2%
Infinite0
Infinite (%)0.0%
Mean146.49696
Minimum0
Maximum2024
Zeros23
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:40.506029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q150
median94
Q3175
95-th percentile407.5
Maximum2024
Range2024
Interquartile range (IQR)125

Descriptive statistics

Standard deviation177.42108
Coefficient of variation (CV)1.2110905
Kurtosis27.346596
Mean146.49696
Median Absolute Deviation (MAD)50
Skewness4.2799321
Sum168618
Variance31478.238
MonotonicityNot monotonic
2024-04-23T11:03:40.810243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 30
 
0.4%
47 24
 
0.3%
0 23
 
0.3%
46 23
 
0.3%
40 21
 
0.3%
100 20
 
0.3%
50 18
 
0.2%
156 16
 
0.2%
65 16
 
0.2%
43 15
 
0.2%
Other values (316) 945
 
12.1%
(Missing) 6648
85.2%
ValueCountFrequency (%)
0 23
0.3%
7 2
 
< 0.1%
9 1
 
< 0.1%
14 5
 
0.1%
17 2
 
< 0.1%
23 1
 
< 0.1%
25 2
 
< 0.1%
26 1
 
< 0.1%
27 5
 
0.1%
29 2
 
< 0.1%
ValueCountFrequency (%)
2024 1
< 0.1%
1683 1
< 0.1%
1654 1
< 0.1%
1290 1
< 0.1%
1242 1
< 0.1%
1196 1
< 0.1%
1087 1
< 0.1%
1006 1
< 0.1%
1000 1
< 0.1%
999 1
< 0.1%

Garage
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.8%
Missing7687
Missing (%)98.6%
Memory size307.0 KiB
1.0
62 
2.0
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters336
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 62
 
0.8%
2.0 50
 
0.6%
(Missing) 7687
98.6%

Length

2024-04-23T11:03:41.070525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T11:03:41.283884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 62
55.4%
2.0 50
44.6%

Most occurring characters

ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 62
18.5%
2 50
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 62
18.5%
2 50
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 62
18.5%
2 50
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 62
18.5%
2 50
14.9%

Floor #
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)9.7%
Missing7551
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean6.0806452
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:41.473400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile20.3
Maximum75
Range74
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.71603
Coefficient of variation (CV)1.4334055
Kurtosis32.958377
Mean6.0806452
Median Absolute Deviation (MAD)2
Skewness5.0845955
Sum1508
Variance75.969179
MonotonicityNot monotonic
2024-04-23T11:03:41.725683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 62
 
0.8%
4 40
 
0.5%
3 36
 
0.5%
5 26
 
0.3%
6 18
 
0.2%
1 14
 
0.2%
8 10
 
0.1%
7 7
 
0.1%
9 6
 
0.1%
10 5
 
0.1%
Other values (14) 24
 
0.3%
(Missing) 7551
96.8%
ValueCountFrequency (%)
1 14
 
0.2%
2 62
0.8%
3 36
0.5%
4 40
0.5%
5 26
0.3%
6 18
 
0.2%
7 7
 
0.1%
8 10
 
0.1%
9 6
 
0.1%
10 5
 
0.1%
ValueCountFrequency (%)
75 2
< 0.1%
43 1
< 0.1%
36 1
< 0.1%
34 2
< 0.1%
31 1
< 0.1%
27 1
< 0.1%
24 1
< 0.1%
23 2
< 0.1%
21 2
< 0.1%
19 1
< 0.1%

Exposure
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)4.3%
Missing7612
Missing (%)97.6%
Memory size308.1 KiB
N
51 
S
48 
SW
25 
E
20 
SE
14 
Other values (3)
29 

Length

Max length2
Median length1
Mean length1.3262032
Min length1

Characters and Unicode

Total characters248
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
N 51
 
0.7%
S 48
 
0.6%
SW 25
 
0.3%
E 20
 
0.3%
SE 14
 
0.2%
NW 13
 
0.2%
NE 9
 
0.1%
W 7
 
0.1%
(Missing) 7612
97.6%

Length

2024-04-23T11:03:41.986631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T11:03:42.261834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
n 51
27.3%
s 48
25.7%
sw 25
13.4%
e 20
 
10.7%
se 14
 
7.5%
nw 13
 
7.0%
ne 9
 
4.8%
w 7
 
3.7%

Most occurring characters

ValueCountFrequency (%)
S 87
35.1%
N 73
29.4%
W 45
18.1%
E 43
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 87
35.1%
N 73
29.4%
W 45
18.1%
E 43
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 87
35.1%
N 73
29.4%
W 45
18.1%
E 43
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 87
35.1%
N 73
29.4%
W 45
18.1%
E 43
17.3%

City
Text

Distinct87
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size645.6 KiB
2024-04-23T11:03:42.741743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length51
Mean length27.747916
Min length3

Characters and Unicode

Total characters216406
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowAdjala-Tosorontio
2nd rowAdjala-Tosorontio
3rd rowAdjala-Tosorontio
4th rowAdjala-Tosorontio
5th rowAdjala-Tosorontio
ValueCountFrequency (%)
metropolitan 6637
25.8%
area 6637
25.8%
toronto 2781
10.8%
1677
 
6.5%
ottawa 903
 
3.5%
gatineau 903
 
3.5%
barrie 395
 
1.5%
st 293
 
1.1%
catharines 293
 
1.1%
niagara 293
 
1.1%
Other values (106) 4934
19.2%
2024-04-23T11:03:43.503381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 29045
13.4%
o 25061
11.6%
t 21629
10.0%
r 20159
9.3%
e 18226
8.4%
17947
 
8.3%
n 13221
 
6.1%
i 10329
 
4.8%
l 8600
 
4.0%
p 6855
 
3.2%
Other values (40) 45334
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 29045
13.4%
o 25061
11.6%
t 21629
10.0%
r 20159
9.3%
e 18226
8.4%
17947
 
8.3%
n 13221
 
6.1%
i 10329
 
4.8%
l 8600
 
4.0%
p 6855
 
3.2%
Other values (40) 45334
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 29045
13.4%
o 25061
11.6%
t 21629
10.0%
r 20159
9.3%
e 18226
8.4%
17947
 
8.3%
n 13221
 
6.1%
i 10329
 
4.8%
l 8600
 
4.0%
p 6855
 
3.2%
Other values (40) 45334
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 29045
13.4%
o 25061
11.6%
t 21629
10.0%
r 20159
9.3%
e 18226
8.4%
17947
 
8.3%
n 13221
 
6.1%
i 10329
 
4.8%
l 8600
 
4.0%
p 6855
 
3.2%
Other values (40) 45334
20.9%
Distinct171
Distinct (%)2.2%
Missing1
Missing (%)< 0.1%
Memory size509.4 KiB
2024-04-23T11:03:44.006496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length31
Median length29
Mean length9.7391639
Min length4

Characters and Unicode

Total characters75946
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowColgan
2nd rowColgan
3rd rowColgan
4th rowColgan
5th rowColgan
ValueCountFrequency (%)
toronto 1094
 
10.0%
ottawa 752
 
6.9%
caledon 269
 
2.5%
barrie 216
 
2.0%
markham 202
 
1.8%
rural 187
 
1.7%
st 151
 
1.4%
lakes 150
 
1.4%
kawartha 150
 
1.4%
guelph 145
 
1.3%
Other values (218) 7641
69.7%
2024-04-23T11:03:44.785784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7698
 
10.1%
o 7487
 
9.9%
t 6126
 
8.1%
r 5843
 
7.7%
e 5245
 
6.9%
n 5189
 
6.8%
l 3873
 
5.1%
i 3675
 
4.8%
3159
 
4.2%
s 2621
 
3.5%
Other values (45) 25030
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7698
 
10.1%
o 7487
 
9.9%
t 6126
 
8.1%
r 5843
 
7.7%
e 5245
 
6.9%
n 5189
 
6.8%
l 3873
 
5.1%
i 3675
 
4.8%
3159
 
4.2%
s 2621
 
3.5%
Other values (45) 25030
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7698
 
10.1%
o 7487
 
9.9%
t 6126
 
8.1%
r 5843
 
7.7%
e 5245
 
6.9%
n 5189
 
6.8%
l 3873
 
5.1%
i 3675
 
4.8%
3159
 
4.2%
s 2621
 
3.5%
Other values (45) 25030
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7698
 
10.1%
o 7487
 
9.9%
t 6126
 
8.1%
r 5843
 
7.7%
e 5245
 
6.9%
n 5189
 
6.8%
l 3873
 
5.1%
i 3675
 
4.8%
3159
 
4.2%
s 2621
 
3.5%
Other values (45) 25030
33.0%

Community Name
Text

MISSING 

Distinct393
Distinct (%)5.3%
Missing367
Missing (%)4.7%
Memory size526.6 KiB
2024-04-23T11:03:45.249609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length29
Mean length13.59338
Min length4

Characters and Unicode

Total characters101026
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.5%

Sample

1st rowColgan Crossing
2nd rowColgan Crossing
3rd rowColgan Crossing
4th rowColgan Crossing
5th rowColgan Crossing
ValueCountFrequency (%)
rural 721
 
4.8%
south 437
 
2.9%
caledon 302
 
2.0%
park 287
 
1.9%
the 261
 
1.7%
238
 
1.6%
east 231
 
1.5%
hill 226
 
1.5%
west 207
 
1.4%
estates 201
 
1.3%
Other values (473) 11836
79.2%
2024-04-23T11:03:46.128190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9227
 
9.1%
a 7578
 
7.5%
7515
 
7.4%
t 6533
 
6.5%
r 6279
 
6.2%
l 6165
 
6.1%
o 5873
 
5.8%
n 5819
 
5.8%
i 5712
 
5.7%
s 5458
 
5.4%
Other values (60) 34867
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9227
 
9.1%
a 7578
 
7.5%
7515
 
7.4%
t 6533
 
6.5%
r 6279
 
6.2%
l 6165
 
6.1%
o 5873
 
5.8%
n 5819
 
5.8%
i 5712
 
5.7%
s 5458
 
5.4%
Other values (60) 34867
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9227
 
9.1%
a 7578
 
7.5%
7515
 
7.4%
t 6533
 
6.5%
r 6279
 
6.2%
l 6165
 
6.1%
o 5873
 
5.8%
n 5819
 
5.8%
i 5712
 
5.7%
s 5458
 
5.4%
Other values (60) 34867
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9227
 
9.1%
a 7578
 
7.5%
7515
 
7.4%
t 6533
 
6.5%
r 6279
 
6.2%
l 6165
 
6.1%
o 5873
 
5.8%
n 5819
 
5.8%
i 5712
 
5.7%
s 5458
 
5.4%
Other values (60) 34867
34.5%

Building Type
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size593.7 KiB
Townhouse and Single Family Home
3137 
Single Family Home
2382 
Condo
1463 
Townhouse
409 
Condo and Townhouse
335 
Other values (2)
 
73

Length

Max length39
Median length32
Mean length20.931914
Min length5

Characters and Unicode

Total characters163248
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTownhouse and Single Family Home
2nd rowTownhouse and Single Family Home
3rd rowTownhouse and Single Family Home
4th rowTownhouse and Single Family Home
5th rowTownhouse and Single Family Home

Common Values

ValueCountFrequency (%)
Townhouse and Single Family Home 3137
40.2%
Single Family Home 2382
30.5%
Condo 1463
18.8%
Townhouse 409
 
5.2%
Condo and Townhouse 335
 
4.3%
Condo, Townhouse and Single Family Home 53
 
0.7%
Condo and Single Family Home 20
 
0.3%

Length

2024-04-23T11:03:46.630172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T11:03:47.116760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
single 5592
21.4%
family 5592
21.4%
home 5592
21.4%
townhouse 3934
15.1%
and 3545
13.6%
condo 1871
 
7.2%

Most occurring characters

ValueCountFrequency (%)
18327
11.2%
o 17202
 
10.5%
e 15118
 
9.3%
n 14942
 
9.2%
l 11184
 
6.9%
m 11184
 
6.9%
i 11184
 
6.9%
a 9137
 
5.6%
H 5592
 
3.4%
y 5592
 
3.4%
Other values (11) 43786
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 163248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18327
11.2%
o 17202
 
10.5%
e 15118
 
9.3%
n 14942
 
9.2%
l 11184
 
6.9%
m 11184
 
6.9%
i 11184
 
6.9%
a 9137
 
5.6%
H 5592
 
3.4%
y 5592
 
3.4%
Other values (11) 43786
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 163248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18327
11.2%
o 17202
 
10.5%
e 15118
 
9.3%
n 14942
 
9.2%
l 11184
 
6.9%
m 11184
 
6.9%
i 11184
 
6.9%
a 9137
 
5.6%
H 5592
 
3.4%
y 5592
 
3.4%
Other values (11) 43786
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 163248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18327
11.2%
o 17202
 
10.5%
e 15118
 
9.3%
n 14942
 
9.2%
l 11184
 
6.9%
m 11184
 
6.9%
i 11184
 
6.9%
a 9137
 
5.6%
H 5592
 
3.4%
y 5592
 
3.4%
Other values (11) 43786
26.8%

Selling Status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size490.6 KiB
Selling
7142 
Registration
 
627
Pending
 
30

Length

Max length12
Median length7
Mean length7.4019746
Min length7

Characters and Unicode

Total characters57728
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelling
2nd rowSelling
3rd rowSelling
4th rowSelling
5th rowSelling

Common Values

ValueCountFrequency (%)
Selling 7142
91.6%
Registration 627
 
8.0%
Pending 30
 
0.4%

Length

2024-04-23T11:03:47.583325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T11:03:47.955548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
selling 7142
91.6%
registration 627
 
8.0%
pending 30
 
0.4%

Most occurring characters

ValueCountFrequency (%)
l 14284
24.7%
i 8426
14.6%
n 7829
13.6%
e 7799
13.5%
g 7799
13.5%
S 7142
12.4%
t 1254
 
2.2%
R 627
 
1.1%
s 627
 
1.1%
r 627
 
1.1%
Other values (4) 1314
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 14284
24.7%
i 8426
14.6%
n 7829
13.6%
e 7799
13.5%
g 7799
13.5%
S 7142
12.4%
t 1254
 
2.2%
R 627
 
1.1%
s 627
 
1.1%
r 627
 
1.1%
Other values (4) 1314
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 14284
24.7%
i 8426
14.6%
n 7829
13.6%
e 7799
13.5%
g 7799
13.5%
S 7142
12.4%
t 1254
 
2.2%
R 627
 
1.1%
s 627
 
1.1%
r 627
 
1.1%
Other values (4) 1314
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 14284
24.7%
i 8426
14.6%
n 7829
13.6%
e 7799
13.5%
g 7799
13.5%
S 7142
12.4%
t 1254
 
2.2%
R 627
 
1.1%
s 627
 
1.1%
r 627
 
1.1%
Other values (4) 1314
 
2.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size526.8 KiB
Construction
6387 
Preconstruction
977 
Complete
 
435

Length

Max length15
Median length12
Mean length12.152712
Min length8

Characters and Unicode

Total characters94779
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConstruction
2nd rowConstruction
3rd rowConstruction
4th rowConstruction
5th rowConstruction

Common Values

ValueCountFrequency (%)
Construction 6387
81.9%
Preconstruction 977
 
12.5%
Complete 435
 
5.6%

Length

2024-04-23T11:03:48.277639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-23T11:03:48.720111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
construction 6387
81.9%
preconstruction 977
 
12.5%
complete 435
 
5.6%

Most occurring characters

ValueCountFrequency (%)
o 15163
16.0%
t 15163
16.0%
n 14728
15.5%
r 8341
8.8%
c 8341
8.8%
s 7364
7.8%
u 7364
7.8%
i 7364
7.8%
C 6822
7.2%
e 1847
 
1.9%
Other values (4) 2282
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 15163
16.0%
t 15163
16.0%
n 14728
15.5%
r 8341
8.8%
c 8341
8.8%
s 7364
7.8%
u 7364
7.8%
i 7364
7.8%
C 6822
7.2%
e 1847
 
1.9%
Other values (4) 2282
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 15163
16.0%
t 15163
16.0%
n 14728
15.5%
r 8341
8.8%
c 8341
8.8%
s 7364
7.8%
u 7364
7.8%
i 7364
7.8%
C 6822
7.2%
e 1847
 
1.9%
Other values (4) 2282
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 15163
16.0%
t 15163
16.0%
n 14728
15.5%
r 8341
8.8%
c 8341
8.8%
s 7364
7.8%
u 7364
7.8%
i 7364
7.8%
C 6822
7.2%
e 1847
 
1.9%
Other values (4) 2282
 
2.4%
Distinct367
Distinct (%)4.7%
Missing16
Missing (%)0.2%
Memory size584.4 KiB
2024-04-23T11:03:49.119601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length64
Median length51
Mean length19.802904
Min length5

Characters and Unicode

Total characters154126
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.4%

Sample

1st rowTribute Communities and Greybrook Realty Partners
2nd rowTribute Communities and Greybrook Realty Partners
3rd rowTribute Communities and Greybrook Realty Partners
4th rowTribute Communities and Greybrook Realty Partners
5th rowTribute Communities and Greybrook Realty Partners
ValueCountFrequency (%)
homes 4402
 
21.4%
communities 853
 
4.1%
developments 827
 
4.0%
group 621
 
3.0%
and 543
 
2.6%
development 394
 
1.9%
fernbrook 271
 
1.3%
ridge 231
 
1.1%
aspen 230
 
1.1%
corporation 224
 
1.1%
Other values (473) 11966
58.2%
2024-04-23T11:03:49.924110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 17962
 
11.7%
15785
 
10.2%
o 14079
 
9.1%
m 8957
 
5.8%
s 8864
 
5.8%
n 8694
 
5.6%
r 8457
 
5.5%
a 8095
 
5.3%
i 8046
 
5.2%
t 7176
 
4.7%
Other values (51) 48011
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17962
 
11.7%
15785
 
10.2%
o 14079
 
9.1%
m 8957
 
5.8%
s 8864
 
5.8%
n 8694
 
5.6%
r 8457
 
5.5%
a 8095
 
5.3%
i 8046
 
5.2%
t 7176
 
4.7%
Other values (51) 48011
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17962
 
11.7%
15785
 
10.2%
o 14079
 
9.1%
m 8957
 
5.8%
s 8864
 
5.8%
n 8694
 
5.6%
r 8457
 
5.5%
a 8095
 
5.3%
i 8046
 
5.2%
t 7176
 
4.7%
Other values (51) 48011
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17962
 
11.7%
15785
 
10.2%
o 14079
 
9.1%
m 8957
 
5.8%
s 8864
 
5.8%
n 8694
 
5.6%
r 8457
 
5.5%
a 8095
 
5.3%
i 8046
 
5.2%
t 7176
 
4.7%
Other values (51) 48011
31.2%

Ceiling avg
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)0.2%
Missing2187
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean8.9893086
Minimum8
Maximum14.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.1 KiB
2024-04-23T11:03:50.204157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.5
Q18.5
median9
Q39
95-th percentile10
Maximum14.5
Range6.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.70183944
Coefficient of variation (CV)0.078074908
Kurtosis18.12397
Mean8.9893086
Median Absolute Deviation (MAD)0.5
Skewness3.4339008
Sum50448
Variance0.4925786
MonotonicityNot monotonic
2024-04-23T11:03:50.692217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 2351
30.1%
8.5 1937
24.8%
9.5 763
 
9.8%
10 224
 
2.9%
8 175
 
2.2%
10.5 62
 
0.8%
13.5 38
 
0.5%
13 27
 
0.3%
12 23
 
0.3%
11 8
 
0.1%
Other values (2) 4
 
0.1%
(Missing) 2187
28.0%
ValueCountFrequency (%)
8 175
 
2.2%
8.5 1937
24.8%
9 2351
30.1%
9.5 763
 
9.8%
10 224
 
2.9%
10.5 62
 
0.8%
11 8
 
0.1%
12 23
 
0.3%
13 27
 
0.3%
13.5 38
 
0.5%
ValueCountFrequency (%)
14.5 2
 
< 0.1%
14 2
 
< 0.1%
13.5 38
 
0.5%
13 27
 
0.3%
12 23
 
0.3%
11 8
 
0.1%
10.5 62
 
0.8%
10 224
 
2.9%
9.5 763
 
9.8%
9 2351
30.1%

Interactions

2024-04-23T11:03:32.499955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:17.267553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:19.302550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:22.557562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:24.778313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:26.627344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:28.576586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:30.704664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:32.760214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:17.531334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:19.548140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:22.881259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:25.040320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:26.871305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:28.839610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:30.925576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:33.123194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:17.784548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:19.778647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:23.145410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:25.274585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:27.119118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:29.085263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:31.167694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:33.470163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:18.041540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:20.135776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:23.401029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:25.521909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:27.370544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:29.350290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:31.390655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:33.797972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:18.285226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:20.534230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:23.800405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:25.766366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:27.613388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:29.525603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:31.578923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:34.145828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:18.538953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:20.911616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:24.052618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:25.999492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:27.853400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:29.770128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:31.807067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:34.494247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:18.806823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:21.534133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:24.298954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:26.198103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:28.108188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:30.199682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:32.028856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:34.816978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:19.055187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:21.914477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:24.525396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:26.379788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:28.333549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:30.451525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-23T11:03:32.254302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-23T11:03:50.913387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Balcony Size SqFtBathsBed RoomsBuilding TypeCeiling avgConstruction StatusExposureFloor #GarageInterior Size SqFtLot Size FtPrice $Selling StatusType
Balcony Size SqFt1.0000.3480.4090.0000.1530.1030.1940.1790.0000.475NaN0.4120.1280.228
Baths0.3481.0000.7280.2280.0240.2010.334-0.0580.1740.7660.3280.6130.0360.274
Bed Rooms0.4090.7281.0000.283-0.0450.2460.2640.0890.4060.8530.1550.5290.0610.334
Building Type0.0000.2280.2831.000-0.1300.3150.4430.0721.0000.498-0.2500.0660.1990.484
Ceiling avg0.1530.024-0.045-0.1301.0000.2160.389-0.0970.000-0.001-0.0640.1390.1420.223
Construction Status0.1030.2010.2460.3150.2161.0000.189-0.0680.000-0.213-0.046-0.0330.1700.321
Exposure0.1940.3340.2640.4430.3890.1891.0000.1010.0000.207NaN0.1061.0000.218
Floor #0.179-0.0580.0890.072-0.097-0.0680.1011.0000.000-0.099NaN0.1231.0000.215
Garage0.0000.1740.4061.0000.0000.0000.0000.0001.0000.6670.8340.4250.0000.549
Interior Size SqFt0.4750.7660.8530.498-0.001-0.2130.207-0.0990.6671.0000.4440.6180.0630.360
Lot Size FtNaN0.3280.155-0.250-0.064-0.046NaNNaN0.8340.4441.0000.3280.1130.454
Price $0.4120.6130.5290.0660.139-0.0330.1060.1230.4250.6180.3281.0000.0390.153
Selling Status0.1280.0360.0610.1990.1420.1701.0001.0000.0000.0630.1130.0391.0000.122
Type0.2280.2740.3340.4840.2230.3210.2180.2150.5490.3600.4540.1530.1221.000

Missing values

2024-04-23T11:03:35.399777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-23T11:03:36.170104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-23T11:03:36.643660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TypeBed RoomsBathsInterior Size SqFtLot Size FtPrice $Balcony Size SqFtGarageFloor #ExposureCitySub AreaCommunity NameBuilding TypeSelling StatusConstruction StatusBuilder(s)Ceiling avg
0Two Story3.031844.0NaN1149990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
1Two Story3.031844.0NaN1159990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
2Two Story3.031847.0NaN1159990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
3Two Story3.031847.0NaN1149990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
4Two Story3.031847.0NaN1159990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
5Two Story3.031847.0NaN1149990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
6Two Story3.032051.0NaN1219990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
7Two Story3.032053.0NaN1209990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
8Two Story3.032174.0NaN1239990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
9Two Story3.032178.0NaN1229990NaNNaNNaNNaNAdjala-TosorontioColganColgan CrossingTownhouse and Single Family HomeSellingConstructionTribute Communities and Greybrook Realty Partners8.5
TypeBed RoomsBathsInterior Size SqFtLot Size FtPrice $Balcony Size SqFtGarageFloor #ExposureCitySub AreaCommunity NameBuilding TypeSelling StatusConstruction StatusBuilder(s)Ceiling avg
7789Detached Two Story4.022279.0NaN984000NaNNaNNaNNaNThe Village FarmThe Village Farm, EmbroNaNSingle Family HomeSellingConstructionLanglois Eco Homesand Sinclair Homes9.0
7790Detached Two Story4.022519.0NaN999000NaNNaNNaNNaNThe Village FarmThe Village Farm, EmbroNaNSingle Family HomeSellingConstructionLanglois Eco Homesand Sinclair Homes9.0
7791Detached Two Story4.022519.0NaN1026000NaNNaNNaNNaNThe Village FarmThe Village Farm, EmbroNaNSingle Family HomeSellingConstructionLanglois Eco Homesand Sinclair Homes9.0
7792Detached Two Story3.021868.042.0799900NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5
7793Detached Two Story3.022017.042.0849900NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5
7794Detached Two Story3.032107.042.0899900NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5
7795Detached Two Story4.022107.042.0854900NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5
7796Detached Two Story4.022495.042.0950000NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5
7797Detached Two Story4.032643.042.01052980NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5
7798Detached Two Story5.032543.042.01018200NaNNaNNaNNaNZorraThames Springs WestThames Springs WestSingle Family HomeSellingConstructionMcKenzie Homes8.5

Duplicate rows

Most frequently occurring

TypeBed RoomsBathsInterior Size SqFtLot Size FtPrice $Balcony Size SqFtGarageFloor #ExposureCitySub AreaCommunity NameBuilding TypeSelling StatusConstruction StatusBuilder(s)Ceiling avg# duplicates
132Suite2.021285.0NaN579990NaNNaN2.0NaNHamilton (Metropolitan area)HamiltonBinbrookCondoSellingCompleteHomes John Bruce RobinsonNaN7
180Two Story3.031740.0NaN699900NaNNaNNaNNaNHastingsHastingsLock EighteenTownhouseSellingConstructionBrenbrooke Homes9.06
24Detached Bungalow3.021700.050.01121900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.05
203Two Story4.032180.0NaN1269900NaNNaNNaNNaNCollingwood (Metropolitan area)Collingwood645 Sixth Street TownhomesTownhouseSellingPreconstructionDream Maker Developments Inc. and Inception BuildsNaN5
13Detached Bungalow2.021342.042.0938900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.04
14Detached Bungalow2.021433.042.0963900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.04
15Detached Bungalow2.021462.035.0859900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.04
16Detached Bungalow2.021500.042.01042900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.04
18Detached Bungalow2.021640.042.01044900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.04
25Detached Bungalow3.022079.050.01179900NaNNaNNaNNaNOttawa - Gatineau (Metropolitan area)OttawaAvalonTownhouse and Single Family HomeSellingConstructioneQ Homes9.04